BUSI 721
Jones Graduate School of Business
Rice University
Kerry Back
\[\text{expected return}-\frac{1}{2}A\times\text{variance}\]
S = np.diag([sd1, sd2, sd3])
R = np.identity(3)
R[0, 1] = R[1, 0] = corr12
R[0, 2] = R[2, 0] = corr13
R[1, 2] = R[2, 1] = corr23
C = S @ R @ S
rprem = np.array([mn1, mn2, mn3]) - rf
Cinv = np.linalg.inv(C)
w = (1/A) * Cinv @ rpremw1=-13.3%, w2=1.1%, w3=62.5%